{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EheA5_j_cEwc"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Probability Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "YCriMWd-pRTP"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "73ztBH5yK_bS"
      },
      "source": [
        "# Multiple changepoint detection and Bayesian model selection\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Qianaf6u_7G_"
      },
      "source": [
        "# Bayseian model selection",
        "\n",
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/probability/examples/Multiple_changepoint_detection_and_Bayesian_model_selection\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Multiple_changepoint_detection_and_Bayesian_model_selection.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Multiple_changepoint_detection_and_Bayesian_model_selection.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/probability/tensorflow_probability/examples/jupyter_notebooks/Multiple_changepoint_detection_and_Bayesian_model_selection.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-o9zA5TO_-hx"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "No2QPkJ1_9z9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow.compat.v2 as tf\n",
        "tf.enable_v2_behavior()\n",
        "import tensorflow_probability as tfp\n",
        "from tensorflow_probability import distributions as tfd\n",
        "\n",
        "from matplotlib import pylab as plt\n",
        "%matplotlib inline\n",
        "import scipy.stats"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UoIGcwDcLK8s"
      },
      "source": [
        "##  Task: changepoint detection with multiple changepoints"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MkPCuGGp464l"
      },
      "source": [
        "Consider a changepoint detection task:  events happen at a rate that changes over time, driven by sudden shifts in the (unobserved) state of some system or process generating the data.\n",
        "\n",
        "For example, we might observe a series of counts like the following:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 285
        },
        "colab_type": "code",
        "id": "kmk8w7-vuKSm",
        "outputId": "26a4f9b6-1ceb-4c01-ecdc-705a3ce5ff41"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[\u003cmatplotlib.lines.Line2D at 0x7fe01d3c2390\u003e]"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCTpBEESO8P8AWlD4bGcec9gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "true_rates = [40, 3, 20, 50]\n",
        "true_durations = [10, 20, 5, 35]\n",
        "\n",
        "observed_counts = np.concatenate([\n",
        "  scipy.stats.poisson(rate).rvs(num_steps)\n",
        "    for (rate, num_steps) in zip(true_rates, true_durations)\n",
        "]).astype(np.float32)\n",
        "\n",
        "plt.plot(observed_counts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TWx9cuas0EcE"
      },
      "source": [
        "These could represent the number of failures in a datacenter, number of visitors to a webpage, number of packets on a network link, etc.\n",
        "\n",
        "Note it's not entirely apparent how many distinct system regimes there are just from looking at the data. Can you tell where each of the three switchpoints occurs?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "09nB0iTzky85"
      },
      "source": [
        "## Known number of states\n",
        "\n",
        "We'll first consider the (perhaps unrealistic) case where the number of unobserved states is known a priori. Here, we'd assume we know there are four latent states.\n",
        "\n",
        "We model this problem as a switching (inhomogeneous) Poisson process: at each point in time, the number of events that occur is Poisson distributed, and the *rate* of events is determined by the unobserved system state $z_t$:\n",
        "\n",
        "$$x_t \\sim \\text{Poisson}(\\lambda_{z_t})$$\n",
        "\n",
        "The latent states are discrete: $z_t \\in \\{1, 2, 3, 4\\}$, so $\\lambda = [\\lambda_1, \\lambda_2, \\lambda_3, \\lambda_4]$ is a simple vector containing a Poisson rate for each state. To model the evolution of states over time, we'll define a simple transition model $p(z_t | z_{t-1})$: let's say that at each step we stay in the previous state with some probability $p$, and with probability $1-p$ we transition to a different state uniformly at random. The initial state is also chosen uniformly at random, so we have:\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "z_1 &\\sim \\text{Categorical}\\left(\\left\\{\\frac{1}{4}, \\frac{1}{4}, \\frac{1}{4}, \\frac{1}{4}\\right\\}\\right)\\\\\n",
        "z_t | z_{t-1} &\\sim \\text{Categorical}\\left(\\left\\{\\begin{array}{cc}p & \\text{if } z_t = z_{t-1} \\\\ \\frac{1-p}{4-1} & \\text{otherwise}\\end{array}\\right\\}\\right)\n",
        "\\end{align*}$$\n",
        "\n",
        "These assumptions correspond to a [hidden Markov model](http://mlg.eng.cam.ac.uk/zoubin/papers/ijprai.pdf) with Poisson emissions. We can encode them in TFP using `tfd.HiddenMarkovModel`. First, we define the transition matrix and the uniform prior on the initial state:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 135
        },
        "colab_type": "code",
        "id": "0qs_l4p4nygq",
        "outputId": "381dd947-787e-4b1e-afce-d473a96661f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initial state logits:\n",
            "[0. 0. 0. 0.]\n",
            "Transition matrix:\n",
            "[[0.95       0.01666667 0.01666667 0.01666667]\n",
            " [0.01666667 0.95       0.01666667 0.01666667]\n",
            " [0.01666667 0.01666667 0.95       0.01666667]\n",
            " [0.01666667 0.01666667 0.01666667 0.95      ]]\n"
          ]
        }
      ],
      "source": [
        "num_states = 4\n",
        "\n",
        "initial_state_logits = np.zeros([num_states], dtype=np.float32) # uniform distribution\n",
        "\n",
        "daily_change_prob = 0.05\n",
        "transition_probs = daily_change_prob / (num_states-1) * np.ones(\n",
        "    [num_states, num_states], dtype=np.float32)\n",
        "np.fill_diagonal(transition_probs,\n",
        "                 1-daily_change_prob)\n",
        "\n",
        "print(\"Initial state logits:\\n{}\".format(initial_state_logits))\n",
        "print(\"Transition matrix:\\n{}\".format(transition_probs))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vWshnDRepxaT"
      },
      "source": [
        "Next, we build a `tfd.HiddenMarkovModel` distribution, using a trainable variable to represent the rates associated with each system state. We parameterize the rates in log-space to ensure they are positive-valued."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "bvEpqBxvoleY"
      },
      "outputs": [],
      "source": [
        "# Define variable to represent the unknown log rates.\n",
        "trainable_log_rates = tf.Variable(\n",
        "  np.log(np.mean(observed_counts)) + tf.random.normal([num_states]),\n",
        "  name='log_rates')\n",
        "\n",
        "hmm = tfd.HiddenMarkovModel(\n",
        "  initial_distribution=tfd.Categorical(\n",
        "      logits=initial_state_logits),\n",
        "  transition_distribution=tfd.Categorical(probs=transition_probs),\n",
        "  observation_distribution=tfd.Poisson(log_rate=trainable_log_rates),\n",
        "  num_steps=len(observed_counts))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4JA6D9EsqNTe"
      },
      "source": [
        "Finally, we define the model's total log density, including a weakly-informative LogNormal prior on the rates, and run an optimizer to compute the [maximum a posteriori](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) (MAP) fit to the observed count data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "6mirKxnNqJSu"
      },
      "outputs": [],
      "source": [
        "rate_prior = tfd.LogNormal(5, 5)\n",
        "\n",
        "def log_prob():\n",
        " return (tf.reduce_sum(rate_prior.log_prob(tf.math.exp(trainable_log_rates))) +\n",
        "         hmm.log_prob(observed_counts))\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.1)\n",
        "\n",
        "@tf.function(autograph=False)\n",
        "def train_op():\n",
        "  with tf.GradientTape() as tape:\n",
        "    neg_log_prob = -log_prob()\n",
        "  grads = tape.gradient(neg_log_prob, [trainable_log_rates])[0]\n",
        "  optimizer.apply_gradients([(grads, trainable_log_rates)])\n",
        "  return neg_log_prob, tf.math.exp(trainable_log_rates)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 236
        },
        "colab_type": "code",
        "id": "gSjyTtkDrOHu",
        "outputId": "97e77b1a-d8f3-4f69-8086-f7a5dc4f1308"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "step 0: log prob -539.8363037109375 rates [ 60.120716  46.096066 115.06791   21.380898]\n",
            "step 20: log prob -251.49668884277344 rates [49.44194  22.835463 42.680107  4.509814]\n",
            "step 40: log prob -244.906982421875 rates [49.37409   22.341614  39.15951    2.7087321]\n",
            "step 60: log prob -244.69183349609375 rates [49.476925  21.316504  40.08259    2.7874506]\n",
            "step 80: log prob -244.27362060546875 rates [49.519707  22.292286  40.211983   3.0713978]\n",
            "step 100: log prob -244.25796508789062 rates [49.507477  22.055244  40.16306    3.1456223]\n",
            "step 120: log prob -244.2534942626953 rates [49.510178  21.981022  40.136353   3.1146054]\n",
            "step 140: log prob -244.2531280517578 rates [49.514404  22.00701   40.136944   3.1029546]\n",
            "step 160: log prob -244.25303649902344 rates [49.513     22.018309  40.144352   3.1072166]\n",
            "step 180: log prob -244.25303649902344 rates [49.51299   22.01862   40.142887   3.1083844]\n",
            "step 200: log prob -244.2530517578125 rates [49.51313   22.017632  40.142963   3.1076818]\n",
            "Inferred rates: [49.51313   22.017632  40.142963   3.1076818]\n",
            "True rates: [40, 3, 20, 50]\n"
          ]
        }
      ],
      "source": [
        "for step in range(201):\n",
        "  loss, rates = [t.numpy() for t in train_op()]\n",
        "  if step % 20 == 0:\n",
        "    print(\"step {}: log prob {} rates {}\".format(step, -loss, rates))\n",
        "\n",
        "print(\"Inferred rates: {}\".format(rates))\n",
        "print(\"True rates: {}\".format(true_rates))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9kGRv8gwrtP5"
      },
      "source": [
        "It worked! Note that the latent states in this model are identifiable only up to permutation, so the rates we recovered are in a different order, and there's a bit of noise, but generally they match pretty well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "43AfcMTjvs7a"
      },
      "source": [
        "### Recovering the state trajectory\n",
        "\n",
        "Now that we've fit the model, we might want to reconstruct *which* state the model believes the system was in at each timestep.\n",
        "\n",
        "This is a *posterior inference* task: given the observed counts $x_{1:T}$ and model parameters (rates) $\\lambda$, we want to infer the sequence of discrete latent variables, following the posterior distribution $p(z_{1:T} | x_{1:T}, \\lambda)$. In a hidden Markov model, we can efficiently compute marginals and other properties of this distribution using standard message-passing algorithms. In particular, the `posterior_marginals` method will efficiently compute (using the [forward-backward algorithm](https://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm)) the marginal probability distribution $p(Z_t = z_t | x_{1:T})$ over the discrete latent state $Z_t$ at each timestep $t$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "IpTbdyah-IyX"
      },
      "outputs": [],
      "source": [
        "# Runs forward-backward algorithm to compute marginal posteriors.\n",
        "posterior_dists = hmm.posterior_marginals(observed_counts)\n",
        "posterior_probs = posterior_dists.probs_parameter().numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cOYMlvssFDwx"
      },
      "source": [
        "Plotting the posterior probabilities, we recover the model's \"explanation\" of the data: at which points in time is each state active?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 731
        },
        "colab_type": "code",
        "id": "oZ7C937t-Xh3",
        "outputId": "6dba937e-9a29-4a6b-e8dd-dbea76dd1d23"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAALKCAYAAAAvY6d9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBArlCATQgghhBBi5f8Drl0qUpQX+qsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 1000x1000 with 8 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def plot_state_posterior(ax, state_posterior_probs, title):\n",
        "  ln1 = ax.plot(state_posterior_probs, c='blue', lw=3, label='p(state | counts)')\n",
        "  ax.set_ylim(0., 1.1)\n",
        "  ax.set_ylabel('posterior probability')\n",
        "  ax2 = ax.twinx()\n",
        "  ln2 = ax2.plot(observed_counts, c='black', alpha=0.3, label='observed counts')\n",
        "  ax2.set_title(title)\n",
        "  ax2.set_xlabel(\"time\")\n",
        "  lns = ln1+ln2\n",
        "  labs = [l.get_label() for l in lns]\n",
        "  ax.legend(lns, labs, loc=4)\n",
        "  ax.grid(True, color='white')\n",
        "  ax2.grid(False)\n",
        "\n",
        "fig = plt.figure(figsize=(10, 10))\n",
        "plot_state_posterior(fig.add_subplot(2, 2, 1),\n",
        "                     posterior_probs[:, 0],\n",
        "                     title=\"state 0 (rate {:.2f})\".format(rates[0]))\n",
        "plot_state_posterior(fig.add_subplot(2, 2, 2),\n",
        "                     posterior_probs[:, 1],\n",
        "                     title=\"state 1 (rate {:.2f})\".format(rates[1]))\n",
        "plot_state_posterior(fig.add_subplot(2, 2, 3),\n",
        "                     posterior_probs[:, 2],\n",
        "                     title=\"state 2 (rate {:.2f})\".format(rates[2]))\n",
        "plot_state_posterior(fig.add_subplot(2, 2, 4),\n",
        "                     posterior_probs[:, 3],\n",
        "                     title=\"state 3 (rate {:.2f})\".format(rates[3]))\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_QhFHJ01NPVj"
      },
      "source": [
        "In this (simple) case, we see that the model is usually quite confident: at most timesteps it assigns essentially all probability mass to a single one of the four states. Luckily, the explanations look reasonable!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "92psCOwMGiQp"
      },
      "source": [
        "We can also visualize this posterior in terms of the rate associated with the *most likely* latent state at each timestep, condensing the probabilistic posterior into a single explanation:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "PsXpBrH3DKbl"
      },
      "outputs": [],
      "source": [
        "most_probable_states = np.argmax(posterior_probs, axis=1)\n",
        "most_probable_rates = rates[most_probable_states]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 312
        },
        "colab_type": "code",
        "id": "CCIwVTnyOcsW",
        "outputId": "26db7d37-bfb8-4609-a5d3-e96fdef53fa7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003cmatplotlib.legend.Legend at 0x7fe01d0afda0\u003e"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAEWCAYAAAAuOkCvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 1000x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 4))\n",
        "ax = fig.add_subplot(1, 1, 1)\n",
        "ax.plot(most_probable_rates, c='green', lw=3, label='inferred rate')\n",
        "ax.plot(observed_counts, c='black', alpha=0.3, label='observed counts')\n",
        "ax.set_ylabel(\"latent rate\")\n",
        "ax.set_xlabel(\"time\")\n",
        "ax.set_title(\"Inferred latent rate over time\")\n",
        "ax.legend(loc=4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4MhfH3a4OBGV"
      },
      "source": [
        "Technical note: instead of the most probable state at each individual timestep, $z^*_t = \\text{argmax}_{z_t} p(z_t | x_{1:T})$, we could have asked for the most probable latent *trajectory*, $z^* = \\text{argmax}_z p(z | x_{1:T})$ (or even samples from the posterior over trajectories!), taking dependence between timesteps into account. To illustrate the difference, suppose a rock-paper-scissors player plays rock 40% of the time, but never twice in a row: rock may be the most likely marginal state at every point in time, but \"rock, rock, rock...'' is definitely *not* the most likely trajectory -- in fact, it has zero probability!\n",
        "\n",
        "TODO(davmre): once `tfp.HiddenMarkovModel` implements the [Viterbi algorithm](https://en.wikipedia.org/wiki/Viterbi_algorithm) to find highest-probability trajectories, update this section to use it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7ytq0tN7tteU"
      },
      "source": [
        "## Unknown number of states\n",
        "\n",
        "In real problems, we may not know the 'true' number of states in the system we're modeling. This may not always be a concern: if you don't particularly care about the identities of the unknown states, you could just run a model with more states than you know the model will need, and learn (something like) a bunch of duplicate copies of the actual states. But let's assume you do care about inferring the 'true' number of latent states.\n",
        "\n",
        "We can view this as a case of [Bayesian model selection](http://alumni.media.mit.edu/~tpminka/statlearn/demo/): we have a set of candidate models, each with a different number of latent states, and we want to choose the one that is most likely to have generated the observed data. To do this, we compute the marginal likelihood of the data under each model (we could also add a prior on the models themselves, but that won't be necessary in this analysis; the [Bayesian Occam's razor](https://www.cs.princeton.edu/courses/archive/fall09/cos597A/papers/MacKay2003-Ch28.pdf) turns out to be sufficient to encode a preference towards simpler models).\n",
        "\n",
        "Unfortunately, the true marginal likelihood, which integrates over both the discrete states $z_{1:T}$ and the (vector of) rate parameters $\\lambda$, $$p(x_{1:T}) = \\int p(x_{1:T}, z_{1:T}, \\lambda) dz d\\lambda,$$ is not tractable for this model. For convenience, we'll approximate it using a so-called \"[empirical Bayes](https://www.cs.ubc.ca/~schmidtm/Courses/540-W16/L19.pdf)\" or \"type II maximum likelihood\" estimate: instead of fully integrating out the (unknown) rate parameters $\\lambda$ associated with each system state, we'll optimize over their values:\n",
        "\n",
        "$$\\tilde{p}(x_{1:T}) = \\max_\\lambda \\int p(x_{1:T}, z_{1:T}, \\lambda) dz$$\n",
        "\n",
        "This approximation may overfit, i.e., it will prefer more complex models than the true marginal likelihood would. We could consider more faithful approximations, e.g., optimizing a variational lower bound, or using a Monte Carlo estimator such as [annealed importance sampling](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/sample_annealed_importance_chain); these are (sadly) beyond the scope of this notebook. (For more on Bayesian model selection and approximations, chapter 7 of the excellent [Machine Learning: a Probabilistic Perspective\n",
        "](https://www.cs.ubc.ca/~murphyk/MLbook/) is a good reference.)\n",
        "\n",
        "In principle, we could do this model comparison simply by rerunning the optimization above many times with different values of `num_states`, but that would be a lot of work. Here we'll show how to consider multiple models in parallel, using TFP's `batch_shape` mechanism for vectorization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dtClNe6fyZAD"
      },
      "source": [
        "**Transition matrix and initial state prior**: rather than building a single model description, now we'll build a *batch* of transition matrices and prior logits, one for each candidate model up to `max_num_states`. For easy batching we'll need to ensure that all computations have the same 'shape': this must correspond to the dimensions of the largest model we'll fit. To handle smaller models, we can  'embed' their descriptions in the topmost dimensions of the state space, effectively treating the remaining dimensions as dummy states that are never used."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 270
        },
        "colab_type": "code",
        "id": "vqyTuY5hrmdR",
        "outputId": "d2002f4c-f293-49aa-c400-a8c38c474132"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of initial_state_logits: (10, 10)\n",
            "Shape of transition probs: (10, 10, 10)\n",
            "Example initial state logits for num_states==3:\n",
            "[   0.    0.    0. -100. -100. -100. -100. -100. -100. -100.]\n",
            "Example transition_probs for num_states==3:\n",
            "[[0.95  0.025 0.025 0.    0.    0.    0.    0.    0.    0.   ]\n",
            " [0.025 0.95  0.025 0.    0.    0.    0.    0.    0.    0.   ]\n",
            " [0.025 0.025 0.95  0.    0.    0.    0.    0.    0.    0.   ]\n",
            " [0.    0.    0.    1.    0.    0.    0.    0.    0.    0.   ]\n",
            " [0.    0.    0.    0.    1.    0.    0.    0.    0.    0.   ]\n",
            " [0.    0.    0.    0.    0.    1.    0.    0.    0.    0.   ]\n",
            " [0.    0.    0.    0.    0.    0.    1.    0.    0.    0.   ]\n",
            " [0.    0.    0.    0.    0.    0.    0.    1.    0.    0.   ]\n",
            " [0.    0.    0.    0.    0.    0.    0.    0.    1.    0.   ]\n",
            " [0.    0.    0.    0.    0.    0.    0.    0.    0.    1.   ]]\n"
          ]
        }
      ],
      "source": [
        "max_num_states = 10\n",
        "\n",
        "def build_latent_state(num_states, max_num_states, daily_change_prob=0.05):\n",
        "\n",
        "  # Give probability exp(-100) ~= 0 to states outside of the current model.\n",
        "  initial_state_logits = -100. * np.ones([max_num_states], dtype=np.float32)\n",
        "  initial_state_logits[:num_states] = 0.\n",
        "\n",
        "  # Build a transition matrix that transitions only within the current\n",
        "  # `num_states` states.\n",
        "  transition_probs = np.eye(max_num_states, dtype=np.float32)\n",
        "  if num_states \u003e 1:\n",
        "    transition_probs[:num_states, :num_states] = (\n",
        "        daily_change_prob / (num_states-1))\n",
        "    np.fill_diagonal(transition_probs[:num_states, :num_states],\n",
        "                     1-daily_change_prob)\n",
        "  return initial_state_logits, transition_probs\n",
        "\n",
        "# For each candidate model, build the initial state prior and transition matrix.\n",
        "batch_initial_state_logits = []\n",
        "batch_transition_probs = []\n",
        "for num_states in range(1, max_num_states+1):\n",
        "  initial_state_logits, transition_probs = build_latent_state(\n",
        "      num_states=num_states,\n",
        "      max_num_states=max_num_states)\n",
        "  batch_initial_state_logits.append(initial_state_logits)\n",
        "  batch_transition_probs.append(transition_probs)\n",
        "\n",
        "batch_initial_state_logits = np.array(batch_initial_state_logits)\n",
        "batch_transition_probs = np.array(batch_transition_probs)\n",
        "print(\"Shape of initial_state_logits: {}\".format(batch_initial_state_logits.shape))\n",
        "print(\"Shape of transition probs: {}\".format(batch_transition_probs.shape))\n",
        "print(\"Example initial state logits for num_states==3:\\n{}\".format(batch_initial_state_logits[2, :]))\n",
        "print(\"Example transition_probs for num_states==3:\\n{}\".format(batch_transition_probs[2, :, :]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "k9NMBMBq2UQw"
      },
      "source": [
        "Now we proceed similarly as above. This time we'll use an extra batch dimension in `trainable_rates` to separately fit the rates for each model under consideration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Ok-3Nzt1suyw"
      },
      "outputs": [],
      "source": [
        "trainable_log_rates = tf.Variable(\n",
        "    (np.log(np.mean(observed_counts)) *\n",
        "     np.ones([batch_initial_state_logits.shape[0], max_num_states]) +\n",
        "     tf.random.normal([1, max_num_states])),\n",
        "     name='log_rates')\n",
        "    \n",
        "hmm = tfd.HiddenMarkovModel(\n",
        "  initial_distribution=tfd.Categorical(\n",
        "      logits=batch_initial_state_logits),\n",
        "  transition_distribution=tfd.Categorical(probs=batch_transition_probs),\n",
        "  observation_distribution=tfd.Poisson(log_rate=trainable_log_rates),\n",
        "  num_steps=len(observed_counts))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eC5vFBX12PvA"
      },
      "source": [
        "In computing the total log prob, we are careful to sum over only the priors for the rates actually used by each model component:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ly0mT_mqdubx"
      },
      "outputs": [],
      "source": [
        "rate_prior = tfd.LogNormal(5, 5)\n",
        "\n",
        "def log_prob():\n",
        "  prior_lps = rate_prior.log_prob(tf.math.exp(trainable_log_rates))\n",
        "  prior_lp = tf.stack(\n",
        "      [tf.reduce_sum(prior_lps[i, :i+1]) for i in range(max_num_states)])\n",
        "  return prior_lp + hmm.log_prob(observed_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "PR5zL24UDkPW"
      },
      "outputs": [],
      "source": [
        "@tf.function(autograph=False)\n",
        "def train_op():\n",
        "  with tf.GradientTape() as tape:\n",
        "    neg_log_prob = -log_prob()\n",
        "  grads = tape.gradient(neg_log_prob, [trainable_log_rates])[0]\n",
        "  optimizer.apply_gradients([(grads, trainable_log_rates)])\n",
        "  return neg_log_prob, tf.math.exp(trainable_log_rates)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yPqvJ9TS5F98"
      },
      "source": [
        "Now we optimize the *batch* objective we've constructed, fitting all candidate models simultaneously:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 388
        },
        "colab_type": "code",
        "id": "hAb22rYe1K_O",
        "outputId": "724a59a2-dbb9-4385-c7ae-eb3733445538"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "step 0: loss [932.1646  832.1462  828.4412  837.84705 846.1599  417.9551  419.0383\n",
            " 417.9084  425.87964 366.82443]\n",
            "step 20: loss [807.2557  286.25125 257.90186 256.22498 262.71576 270.11447 279.765\n",
            " 267.6623  276.87903 284.21024]\n",
            "step 40: loss [803.20874 279.9999  254.07617 254.04176 262.7116  257.1419  265.64944\n",
            " 263.69067 270.1178  278.18292]\n",
            "step 60: loss [803.0721  271.3898  244.30405 244.61728 252.61594 256.8096  263.2815\n",
            " 261.81732 268.69806 277.49   ]\n",
            "step 80: loss [802.9     271.44714 244.07954 244.36232 252.20078 256.4545  261.7729\n",
            " 260.18414 267.57748 276.11465]\n",
            "step 100: loss [802.9022  271.3092  243.98022 244.25925 251.41597 256.0926  260.91467\n",
            " 257.74948 265.95273 275.0009 ]\n",
            "step 120: loss [802.8999  271.30957 243.9751  244.25511 250.79546 255.72038 260.2765\n",
            " 256.68982 264.83832 274.82697]\n",
            "step 140: loss [802.89966 271.3077  243.97357 244.25308 250.65877 255.32945 259.61533\n",
            " 255.69922 264.014   274.46405]\n",
            "step 160: loss [802.8996  271.3077  243.97354 244.25311 250.64474 254.89218 258.482\n",
            " 254.8172  263.24512 273.9937 ]\n",
            "step 180: loss [802.8997  271.3077  243.97356 244.25302 250.64305 253.76013 257.86176\n",
            " 254.0182  262.5052  273.49908]\n",
            "step 200: loss [802.89954 271.3077  243.9734  244.2529  250.64278 253.11316 257.45514\n",
            " 253.2917  261.7873  273.0126 ]\n"
          ]
        }
      ],
      "source": [
        "for step in range(201):\n",
        "  loss, rates =  [t.numpy() for t in train_op()]\n",
        "  if step % 20 == 0:\n",
        "    print(\"step {}: loss {}\".format(step, loss))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 312
        },
        "colab_type": "code",
        "id": "_Jsthql_IxhW",
        "outputId": "e9f65ce2-2752-4ae9-8653-8829d6644e93"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Model selection on latent states')"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEWCAYAAABFSLFOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYRZ/T9ccqAwrZGtfAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "num_states = np.arange(1, max_num_states+1)\n",
        "plt.plot(num_states, -loss)\n",
        "plt.ylim([-400, -200])\n",
        "plt.ylabel(\"marginal likelihood $\\\\tilde{p}(x)$\")\n",
        "plt.xlabel(\"number of latent states\")\n",
        "plt.title(\"Model selection on latent states\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Kq7SKiR-6c1l"
      },
      "source": [
        "Examining the likelihoods, we see that the (approximate) marginal likelihood prefers a three- or four-state model (the specific ordering may vary between runs of this notebook). This seems quite plausible -- the 'true' model had four states, but from just looking at the data it's hard to rule out a three-state explanation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "u0tqU6Lo6pFD"
      },
      "source": [
        "We can also extract the rates fit for each candidate model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 253
        },
        "colab_type": "code",
        "id": "lnXTiGX4d6e4",
        "outputId": "7c26811f-ad32-4cad-dc61-1d0f315174d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "rates for 1-state model: [32.98682]\n",
            "rates for 2-state model: [44.949432   3.1397576]\n",
            "rates for 3-state model: [22.019554   3.1073658 47.4462   ]\n",
            "rates for 4-state model: [22.01749    3.1073527 49.513165  40.14324  ]\n",
            "rates for 5-state model: [22.016987   3.1073353 49.470993  40.127758  49.488205 ]\n",
            "rates for 6-state model: [22.016418    0.12033073 50.25381    40.12794    48.88818     3.1076846 ]\n",
            "rates for 7-state model: [4.9506187e+01 2.2016148e+01 4.9468941e+01 4.7518797e-03 4.9311584e+01\n",
            " 3.1077573e+00 4.0113823e+01]\n",
            "rates for 8-state model: [4.0115150e+01 4.3629836e-02 4.9482445e+01 4.5004072e-05 3.1080871e+00\n",
            " 5.0322604e-01 4.9483521e+01 2.2015779e+01]\n",
            "rates for 9-state model: [4.0034302e+01 7.8987077e-02 4.9487354e+01 3.3131179e-03 4.0034004e+01\n",
            " 4.7514364e-01 4.9488628e+01 2.2016052e+01 3.1080682e+00]\n",
            "rates for 10-state model: [39.950623    3.0235524  39.950375    0.16000797 39.950424   21.830935\n",
            " 21.831202    3.0232046  49.51654     3.0235553 ]\n"
          ]
        }
      ],
      "source": [
        "for i, learned_model_rates in enumerate(rates):\n",
        "  print(\"rates for {}-state model: {}\".format(i+1, learned_model_rates[:i+1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8eArj7lke9Ei"
      },
      "source": [
        "And plot the explanations each model provides for the data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "XEuhytSKcn4g"
      },
      "outputs": [],
      "source": [
        "posterior_probs = hmm.posterior_marginals(\n",
        "    observed_counts).probs_parameter().numpy()\n",
        "most_probable_states = np.argmax(posterior_probs, axis=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 874
        },
        "colab_type": "code",
        "id": "g3RiZCjzuL8o",
        "outputId": "43717bab-1eff-4ed5-83e3-8fea9048fd4f"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+kAAANZCAYAAABz938KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niRH/H45jgfm8Io7NAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 1400x1200 with 10 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(14, 12))\n",
        "for i, learned_model_rates in enumerate(rates):\n",
        "  ax = fig.add_subplot(4, 3, i+1)\n",
        "  ax.plot(learned_model_rates[most_probable_states[i]], c='green', lw=3, label='inferred rate')\n",
        "  ax.plot(observed_counts, c='black', alpha=0.3, label='observed counts')\n",
        "  ax.set_ylabel(\"latent rate\")\n",
        "  ax.set_xlabel(\"time\")\n",
        "  ax.set_title(\"{}-state model\".format(i+1))\n",
        "  ax.legend(loc=4)\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "sw25-htzfxLZ"
      },
      "source": [
        "It's easy to see how the one-, two-, and (more subtly) three-state models provide inadequate explanations. Interestingly, all models above four states provide essentially the same explanation! This is likely because our 'data' is relatively clean and leaves little room for alternative explanations; on messier real-world data we would expect the higher-capacity models to provide progressively better fits to the data, with some tradeoff point where the improved fit is outweighted by model complexity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fY5E0BaPI7lz"
      },
      "source": [
        "### Extensions\n",
        "\n",
        "The models in this notebook could be straightforwardly extended in many ways. For example:\n",
        "\n",
        "- allowing latent states to have different probabilities (some states may be common vs rare)\n",
        "- allowing nonuniform transitions between latent states (e.g., to learn that a machine crash is usually followed by a system reboot is usually followed by a period of good performance, etc.)\n",
        "- other emission models, e.g. `NegativeBinomial` to model varying dispersions in count data, or continous distributions such as `Normal` for real-valued data.\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Multiple changepoint detection and Bayesian model selection",
      "private_outputs": false,
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
